class_docstr: Scales node positions by a randomly sampled factor :math:`s` within a
  given interval, *e.g.*, resulting in the transformation matrix
  (functional name: :obj:`random_scale`).
  
  .. math::
      \begin{bmatrix}
          s & 0 & 0 \\
          0 & s & 0 \\
          0 & 0 & s \\
      \end{bmatrix}
  
  for three-dimensional positions.
  
  Args:
      scales (tuple): scaling factor interval, e.g. :obj:`(a, b)`, then scale
          is randomly sampled from the range
          :math:`a \leq \mathrm{scale} \leq b`.
code: def __init__(self, scales: Tuple[float, float]) -> None:
      assert isinstance(scales, (tuple, list)) and len(scales) == 2
      self.scales = scales
  
file_path: pytorch_geometric/torch_geometric/transforms/random_scale.py
imports: import random
  from typing import Tuple
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 1 to create a new RandomScale object from inherited base classes: BaseTransform with example: obj = RandomScale(scales)